Image and Signal Processing Methods

Extended census transform histogram for land-use scene classification

[+] Author Affiliations
Baohua Yuan

HoHai University, School of Computer and Information Engineering, Nanjing, China

Nanjing University of Science and Technology, Taizhou Institute of Science, Department of Computer Science and Technology, Taizhou, China

Shijin Li

HoHai University, School of Computer and Information Engineering, Nanjing, China

J. Appl. Remote Sens. 11(2), 025003 (Apr 25, 2017). doi:10.1117/1.JRS.11.025003
History: Received January 9, 2017; Accepted April 3, 2017
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Abstract.  With the popular use of high-resolution satellite images, more and more research efforts have been focused on land-use scene classification. In scene classification, effective visual features can significantly boost the final performance. As a typical texture descriptor, the census transform histogram (CENTRIST) has emerged as a very powerful tool due to its effective representation ability. However, the most prominent limitation of CENTRIST is its small spatial support area, which may not necessarily be adept at capturing the key texture characteristics. We propose an extended CENTRIST (eCENTRIST), which is made up of three subschemes in a greater neighborhood scale. The proposed eCENTRIST not only inherits the advantages of CENTRIST but also encodes the more useful information of local structures. Meanwhile, multichannel eCENTRIST, which can capture the interactions from multichannel images, is developed to obtain higher categorization accuracy rates. Experimental results demonstrate that the proposed method can achieve competitive performance when compared to state-of-the-art methods.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Baohua Yuan and Shijin Li
"Extended census transform histogram for land-use scene classification", J. Appl. Remote Sens. 11(2), 025003 (Apr 25, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.025003


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